Identification of crucial genes mediating abdominal aortic aneurysm pathogenesis based on gene expression profiling of perivascular adipose tissue by WGCNA

Siliang Chen; Dan Yang; Bao Liu; Yuexin Chen; Ye We; Mengyin Chen; Yuehong Zheng

doi:10.21037/atm-20-3758

January 1, 2021Annals of Translational MedicineOpen Access

Identification of crucial genes mediating abdominal aortic aneurysm pathogenesis based on gene expression profiling of perivascular adipose tissue by WGCNA

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Why the study?

Ruptured AAA carries a 65-85% mortality rate with few effective pharmaceutical treatments, making the identification of pathogenesis and molecular targets essential.

Structured PICO

P

Population

mRNA microarray dataset of perivascular adipose tissue (PVAT) in abdominal aortic aneurysm (AAA) patients (dilated and non-dilated samples)

I

Intervention

Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening

C

Comparator

Dilated vs. non-dilated PVAT samples

O

Outcome

Identification of differentially expressed genes (DEGs), gene modules, and hub genes related to AAA pathogenesissurrogate

Identified MAP4K1, PROK2, and PLAU as crucial genes in the pathogenesis of abdominal aortic aneurysm using gene expression profiling of perivascular adipose tissue.

Limitations

Future in vitro and in vivo experiments are required to more comprehensively explore the biological mechanisms

Implication

May identify PVAT targets for AAA therapy; hypothesis-generating and should not yet change practice.

Abstract

Background: With a mortality rate of 65–85%, a ruptured abdominal aortic aneurysm (AAA) can have catastrophic consequences for patients. However, few effective pharmaceutical treatments are available to treat this condition. Therefore, elucidating the pathogenesis of AAA and finding the potential molecular targets for medical therapies are vital lines of research. Methods: An mRNA microarray dataset of perivascular adipose tissue (PVAT) in AAA patients was downloaded and differentially expressed gene (DEG) screening was performed. Weighted gene co-expression networks for dilated and non-dilated PVAT samples were constructed via weighted correlation network analysis (WGCNA) and used to detect gene modules. Functional annotation analysis was performed for the DEGs and gene modules. We identified the hub genes of the modules and created a DEG co-expression network. We then mined crucial genes based on this network using Molecular Complex Detection (MCODE) in Cytoscape. Crucial genes with top-6 degree in the crucial gene cluster were visualized, and their potential clinical significance was determined. Results: Of the 173 DEGs screened, 99 were upregulated and 74 were downregulated. Co-expression networks were built and we detected 6 and 5 modules for dilated and non-dilated PVAT samples, respectively. The turquoise and black modules for dilated PVAT samples were related to inflammation and immune response. MAP4K1 and PROK2 were the hub genes of these 2 modules, respectively. Then a DEG co-expression network with 112 nodes and 953 edges was created. PLAU was the crucial gene with the highest connectivity and showed potential clinical significance. Conclusions: Using WGCNA, gene modules were detected and hub genes and crucial genes were identified. These crucial genes might be potential targets for pharmaceutic therapies and have potential clinical significance. Future in vitro and in vivo experiments are required to more comprehensively explore the biological mechanisms by which these genes affect AAA pathogenesis